{"id":113670,"url":"https://alion.io/job/dyson-lead-data-intelligence-machine-learning-engineer","title":"Lead Data Intelligence Machine Learning Engineer","company":{"id":160,"name":"Dyson","domain":"dyson.com","url":"https://alion.io/company/dyson","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":90,"open_postings":52,"ghost_share":0.038,"stale_share":0.212,"repost_share":0.173,"time_to_fill_p50_days":39,"computed_at":"2026-11-10T06:52:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Dubai, United Arab Emirates"],"countries":["AE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":81000,"max_usd":193000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":773},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Computer Vision","optional":false},{"name":"DVC","optional":false},{"name":"GCP","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Snorkel","optional":false},{"name":"SQL","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Tableau","optional":false},{"name":"TensorFlow","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-07-24T00:00:00Z","employer_posted_date":"2026-11-10","last_verified_at":"2026-11-10T13:41:17Z","board_verified":true,"closed_at":null,"days_open":109,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":109},"description":"About us\nAt Dyson, we’re driven by a relentless pursuit of innovation-pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson’s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.\nYou’ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.\n\nAbout the role\nWe are looking for a specialized Lead Data Intelligence Machine Learning Engineer to design and implement in-house tools that automate our data labelling pipelines. Your primary goal will be to reduce our reliance on manual annotation by leveraging techniques like Active Learning, Weak Supervision, and Synthetic Data Generation. You will bridge the gap between raw data collection and model-ready datasets, ensuring high-quality labels at scale.\nKey Responsibilities\nArchitect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.\n\nDevelop \"Human-in-the-Loop\" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.\n\nQuality Assurance & Denoising: Implement algorithmic checks to identify and correct mislabelled or \"noisy\" data within existing datasets.\n\nTooling & Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.\n\nModel Optimization: Fine-tune \"teacher\" models to generate high-quality pseudo-labels for \"student\" models.\n\nSet up and maintain robust data preparation infrastructure-optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.\n\nPerform data visualization and in-depth analysis using advanced data and feature engineering techniques. You’ll help transform raw data into actionable insight, supporting both research and deployment.\n\nWork closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.\n\nAbout you\nAt least 8+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.\n\nProficiency in Python: Mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).\n\nAutomated Labelling Expertise: Proven experience with Weak Supervision (labelling functions) or Active Learning strategies (uncertainty sampling, diversity sampling).\n\nData Engineering: Experience with SQL and NoSQL databases, and managing large-scale unstructured data (images, text, or audio).\n\nCloud Infrastructure: Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labelling services.\n\nVersion Control for Data: Experience with DVC (Data Version Control) or similar tools to track dataset iterations.\n\nHands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.\n\nAdvanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e.g. TensorFlow, PyTorch, scikit-learn, pandas).\n\nExperience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on-prem, or hybrid).\n\nStrong background in feature engineering, data analysis, and data visualization-comfortable using tools like Jupyter, Tableau, or Power BI.\n\nGreat communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.\n\nAble to balance speed and quality, stay curious about new developments, and deliver results in a fast-moving environment.\n\nBachelor’s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.\n\nDyson is an equal opportunity employer. We know that great minds don’t think alike, and it takes all kinds of minds to make our technology so unique. 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